Jörn-Henrik Jacobsen

According to our database, Jörn-Henrik Jacobsen authored at least 32 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers.

Laura Manduchi, Antoine Wehenkel, Jens Behrmann, Luca Pegolotti, Andy C. Miller, Ozan Sener, Marco Cuturi, Guillermo Sapiro, Jörn-Henrik Jacobsen

CoRR, 2024

Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare.

Arno Blaas, Adam Golinski, Andrew C. Miller, Luca Zappella, Jörn-Henrik Jacobsen, Christina Heinze-Deml

CoRR, 2024

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration.

Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Marco Cuturi, Jörn-Henrik Jacobsen

CoRR, 2024

2023

Robust Hybrid Learning With Expert Augmentation.

Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen

Trans. Mach. Learn. Res., 2023

Simulation-based Inference for Cardiovascular Models.

Antoine Wehenkel, Jens Behrmann, Andrew C. Miller, Guillermo Sapiro, Ozan Sener, Marco Cuturi, Jörn-Henrik Jacobsen

CoRR, 2023

2022

Learning Invariant Representations with Missing Data.

Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Out-of-Distribution Generalization via Risk Extrapolation (REx).

David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville

Proceedings of the 38th International Conference on Machine Learning, 2021

Environment Inference for Invariant Learning.

Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel

Proceedings of the 38th International Conference on Machine Learning, 2021

Understanding and Mitigating Exploding Inverses in Invertible Neural Networks.

Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Shortcut learning in deep neural networks.

Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann

Nat. Mach. Intell., 2020

Exchanging Lessons Between Algorithmic Fairness and Domain Generalization.

Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel

CoRR, 2020

Out-of-Distribution Generalization via Risk Extrapolation (REx).

David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Rémi Le Priol, Aaron C. Courville

CoRR, 2020

Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling.

Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel

CoRR, 2020

How to train your neural ODE.

Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman

CoRR, 2020

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations.

Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling.

Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel

Proceedings of the 37th International Conference on Machine Learning, 2020

How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization.

Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, Adam M. Oberman

Proceedings of the 37th International Conference on Machine Learning, 2020

Your classifier is secretly an energy based model and you should treat it like one.

Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky

Proceedings of the 8th International Conference on Learning Representations, 2020

Understanding the Limitations of Conditional Generative Models.

Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard S. Zemel

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Residual Flows for Invertible Generative Modeling.

Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen

CoRR, 2019

Conditional Generative Models are not Robust.

Ethan Fetaya, Jörn-Henrik Jacobsen, Richard S. Zemel

CoRR, 2019

Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness.

Jörn-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr, Nicolas Papernot

CoRR, 2019

Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks.

Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Jörn-Henrik Jacobsen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Residual Flows for Invertible Generative Modeling.

Tian Qi Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Flexibly Fair Representation Learning by Disentanglement.

Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel

Proceedings of the 36th International Conference on Machine Learning, 2019

Invertible Residual Networks.

Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen

Proceedings of the 36th International Conference on Machine Learning, 2019

Excessive Invariance Causes Adversarial Vulnerability.

Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Invertible Residual Networks.

Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen

CoRR, 2018

i-RevNet: Deep Invertible Networks.

Jörn-Henrik Jacobsen, Arnold W. M. Smeulders, Edouard Oyallon

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Multiscale Hierarchical Convolutional Networks.

Jörn-Henrik Jacobsen, Edouard Oyallon, Stéphane Mallat, Arnold W. M. Smeulders

CoRR, 2017

Dynamic Steerable Blocks in Deep Residual Networks.

Jörn-Henrik Jacobsen, Bert De Brabandere, Arnold W. M. Smeulders

Proceedings of the British Machine Vision Conference 2017, 2017

2016

Structured Receptive Fields in CNNs.

Jörn-Henrik Jacobsen, Jan C. van Gemert, Zhongyu Lou, Arnold W. M. Smeulders

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016